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The communities of Puerto Rico are highly vulnerable to climate change as the archipelago has experienced a multitude of compounding crises and extreme weather events in recent years. To address these issues, the research, analysis, and design of grand challenge solutions for disaster-prone regions like Puerto Rico can utilize collaborative transdisciplinary efforts. Local non-governmental and community-based organizations have a pivotal role in the reconstruction processes and the building of community and environmental resilience in underserved communities. This paper contributes an empirical case study of an online transdisciplinary collaboration between a group of academics and a Puerto Rican non-governmental organization, Caras con Causa. From participant observation, it includes a document analysis of meeting notes with cohort members who were involved in a collaborative National Science Foundation Project, The INFEWS-ER: A Virtual Resource Center Enabling Graduate Innovations at the Nexus of Food, Energy, and Water Systems, with Caras con Causa between October 2020 and April 2021. Caras con Causa focuses on uplifting Puerto Ricans by creating and administering environmental, educational, economic, and community programs, highlighting disaster relief and resilience to help Puerto Rican food, energy, and water systems. Eight key discussion themes emerged from the document analysis: team organization, collaboration with Caras con Causa, deliverables, team contributions, context understanding, participation outcomes, technology setup, and lessons learned. We analyze each of the emerging themes to explain how academics may use transdisciplinary skill sets in addition to standard disciplinary-based approaches or techniques to enhance the institutional capacity of a non-governmental organization doing community resilience work to benefit local food, energy, and water systems. While the learned lessons in this non-governmental organization-academic collaboration may be context-specific, we provide insights that may be generalizable to collaborations in comparable transdisciplinary settings.

This study was designed to assess the psychometric properties of a brief version of the Frankfurt Emotion Work Scale (FEWS) adapted to Spanish in a sample of teachers, taking into consideration gender differences. Method: The sample consisted of 9020 teachers of public education in the Valencian Community (Mage = 45.33 years, SD = 9.15; 72.5% female). Results: The psychometric properties are adequate. It consists of 12 items grouped into six factors that explain 79.54% of the variance. Cronbach's alpha and composite reliability coefficients (CRC) for the full scale are adequate: α = 0.79; CRC = 0.90. Confirmatory Factorial Analyses also confirm the structure of the scale (χ2 = 890.36, S-B χ2 = 747.38, df = 39, NFI = 0.945, NNFI = 0.911, CFI = 0.947, IFI = 0.947, MFI = 0.925, and RMSEA = 0.069). Convergent and discriminant validity were also probed. Finally, a gender effect over Emotional labour was found, there was a difference between gender in the scale and all of the dimensions of the questionnaire, with higher values on women. Conclusions: according to the results of this study, the use of this diagnostic tool for Spanish non-university teachers appears to be justified. [ABSTRACT FROM AUTHOR]

There is a rapidly growing need to communicate to the public and policymakers on the nature and impact of climate change and its associated extremes, which manifest themselves across essential Food-Energy-Water Systems (FEWS). The complexity of this nexus demands analytical tools that can capture the essence of FEWS with the climate system, which may be difficult to stage and implement from a computationally efficient point-of-view. Reduced Complexity Models (RCMs) can synthesize important facets of a system quickly and with less dependence on difficult-to-assign inputs. We report on the development of an RCM framework for the FEWS nexus, to serve as a basic research tool in facilitating parameter sensitivity experiments as well as a means to establish more insightful dialogue with stakeholders through joint scenario construction. Three stand-alone and coupled models at the basin scale have been configured using Stella Architect software to simulate: 1) major flows and storage of water, 2) power plant operations and subsequent impacts on river reaches; and 3) nitrogen (N) mobilization and transport from atmospheric and landmass sources to riverine receiving waters. The Delaware River Basin is chosen for a contemporary simulation test case. Modeled results are calibrated and validated using observed stream gauge data, indicating reliable model performance at the monthly and annual time steps (0.57 < NSE < 0.98). A set of single and multi-factor climate, technology, and policy experiments are then explored using the RCM framework. Basin-scale system sensitivities are investigated across a set of intensified climate extremes, revealing the crucial roles of sewage treatment and energy infrastructure for climate resilience, significant exacerbations as well as mitigations of thermal and N pollution under opposing climate extremes, and important tradeoffs between river temperature and electricity production that are explored with technology and policy scenarios.

Change to global climate, including both its progressive character and episodic extremes, constitutes a critical societal challenge. We apply here a framework to analyze Climate-induced Extremes on the Food, Energy, Water System Nexus (C-FEWS), with particular emphasis on the roles and sensitivities of traditionally-engineered (TEI) and nature-based (NBI) infrastructures. The rationale and technical specifications for the overall C-FEWS framework, its component models and supporting datasets are detailed in an accompanying paper (Vörösmarty et al., this issue). We report here on initial results produced by applying this framework in two important macro-regions of the United States (Northeast, NE; Midwest, MW), where major decisions affecting global food production, biofuels, energy security and pollution abatement require critical scientific support. We present the essential FEWS-related hypotheses that organize our work with an overview of the methodologies and experimental designs applied. We report on initial C-FEWS framework results using five emblematic studies that highlight how various combinations of climate sensitivities, TEI-NBI deployments, technology, and environmental management have determined regional FEWS performance over a historical time period (1980–2019). Despite their relative simplicity, these initial scenario experiments yielded important insights. We found that FEWS performance was impacted by climate stress, but the sensitivity was strongly modified by technology choices applied to both ecosystems (e.g., cropland production using new cultivars) and engineered systems (e.g., thermoelectricity from different fuels and cooling types). We tabulated strong legacy effects stemming from decisions on managing NBI (e.g., multi-decade land conversions that limit long-term carbon sequestration). The framework also enabled us to reveal how broad-scale policies aimed at a particular net benefit can result in unintended and potentially negative consequences. For example, tradeoff modeling experiments identified the regional importance of TEI in the form wastewater treatment and NBI via aquatic self-purification. This finding, in turn, could be used to guide potential investments in point and/or non-point source water pollution control. Another example used a reduced complexity model to demonstrate a FEWS tradeoff in the context of water supply, electricity production, and thermal pollution. Such results demonstrated the importance of TEI and NBI in jointly determining historical FEWS performance, their vulnerabilities, and their resilience to extreme climate events. These infrastructures, plus technology and environmental management, constitute the “policy levers” which can actively be engaged to mitigate the challenge of contemporary and future climate change.

Climate change continues to challenge food, energy, and water systems (FEWS) across the globe and will figure prominently in shaping future decisions on how best to manage this nexus. In turn, traditionally engineered and natural infrastructures jointly support and hence determine FEWS performance, their vulnerabilities, and their resilience in light of extreme climate events. We present here a research framework to advance the modeling, data integration, and assessment capabilities that support hypothesis-driven research on FEWS dynamics cast at the macro-regional scale. The framework was developed to support studies on climate-induced extremes on food, energy, and water systems (C-FEWS) and designed to identify and evaluate response options to extreme climate events in the context of managing traditionally engineered (TEI) and nature-based infrastructures (NBI). This paper presents our strategy for a first stage of research using the framework to analyze contemporary FEWS and their sensitivity to climate drivers shaped by historical conditions (1980–2019). We offer a description of the computational framework, working definitions of the climate extremes analyzed, and example configurations of numerical experiments aimed at evaluating the importance of individual and combined driving variables. Single and multiple factor experiments involving the historical time series enable two categories of outputs to be analyzed: the first involving biogeophysical entities (e.g., crop production, carbon sequestered, nutrient and thermal pollution loads) and the second reflecting a portfolio of services provided by the region’s TEI and NBI, evaluated in economic terms. The framework is exercised in a series of companion papers in this special issue that focus on the Northeast and Midwest regions of the United States. Use of the C-FEWS framework to simulate historical conditions facilitates research to better identify existing FEWS linkages and how they function. The framework also enables a next stage of analysis to be pursued using future scenario pathways that will vary land use, technology deployments, regulatory objectives, and climate trends and extremes. It also supports a stakeholder engagement effort to co-design scenarios of interest beyond the research domain.

A water level sensor is critical to measure water levels at strategic points in the river. The sensor location directly impacts the quality of the collected data sent to the flood early warning system. To prevent and minimize the risk of flooding, it is crucial to determine the optimal locations for water level sensor placement. This research proposes a Mixed-Integer Linear Programming (MILP) model for water level sensor placement considering multi-sensor and multi-disaster areas. In addition, this model was applied in a case study in Tikala River, Manado, Indonesia. The results indicated that all disaster areas could be covered by at least one single sensor. A sensitivity analysis was performed by running the model under several different budget scenarios. When the budget increases, the number of sensors and the coverage performance are getting larger. Thus, the proposed MILP model was able to determine the optimal locations for sensor placement under a limited budget.

With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data. The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the “supply chain” of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions.

Chalcogenide glasses, owing to their transparency in the infrared window and the appropriate solubility of rare earth, allows the generation of middle infrared (mid-IR) radiation from a near infrared or visible pumping source. These emitted mid-IR broad bands can probe the vibrational modes of several molecules, e.g. C H, C O or C Cl. Relying on this principle, a mid-IR optical sensor using the mid-IR fluorescence of Pr 3+ : Ga-Ge-Sb-S fibers has been developed. The detection principle is based on Fiber Evanescent Wave Spectroscopy (FEWS). The spectroscopic characterization of praseodymium ions (Pr 3+ ) was performed in the near and mid-IR and is discussed on the basis of comparison with Judd–Ofelt calculations. The broad emission spectrum of the Pr 3+ : Ga-Ge-Sb-S fiber from 4 to 5 μm could enable the monitoring of multiple pollutants. In this study, chloroform detection is carried out via a novel technique derived from FEWS. In this way, an infrared sensor was developed, composed of a pumping source in near-IR, a mid-IR detector and a tapered Pr 3+ : chalcogenide fiber to enhance the detection sensitivity. These results demonstrate for the first time the feasibility of detecting molecules by FEWS using the mid-IR fluorescence emitted by rare earth ions doping chalcogenide fibers. This method is an effective alternative to the classical FEWS system, as RE doped chalcogenide fibers have the advantage of being a compact mid-IR source. [ABSTRACT FROM AUTHOR]

Using an unclad multimode Ge–Te–Se based chalcogenide glass fibre, simple design robust fibre evanescent wave spectroscopic (FEWS) sensor is demonstrated. Methodologies adopted for material development and fibre drawing are discussed in the following steps: purification of raw materials for high spectral purity, fabrication of glass and fibre preform leading to fibre drawing. The fabricated fibre has a minimum loss of 1.4 dB/m at 4.2 μm, and less than 3 dB/m between 1.5 and 6.3 μm. The feasibility of using such a fibre for evanescent wave spectroscopic sensing has been verified by using the finite-element (FE) computation technique. Supported optical modes as well as corresponding penetration depths of evanescent fields from different modes are discussed. Based on the FE computation, a FEWS sensor consisting of a 40 cm Ge–Te–Se fibre, coupled with a Fourier transform infrared (FTIR) spectrometer and a liquid nitrogen cooled mercury–cadmium–tellurium (MCT) detector, is demonstrated. The active length along this fibre employed for sensing is 3 cm. Based on FEWS design, the fabricated fibre sensor was used for the analysis of chemicals, namely the acetone, ethanol, methanol, tocopherol (vitamin E), ascorbic acid (vitamin C), fresh orange and lemon juice. [ABSTRACT FROM AUTHOR]

Abstract: Fiber evanescent wave spectroscopy (FEWS) was used to classify various foodborne pathogens (Listeria monocytogenes, Staphylococcus aureus and Salmonella enteritidis) using chemometric methods such as principal component analysis (PCA) and logistic-partial least squares (logistic-PLS). Spectra were recorded thanks to an IR optical fiber made of chalcogenide glasses. A limited segregation between the healthy and non healthy food has been observed using the PCA method, whereas a clear differentiation between bacteria strains was obtained using the PLS regression. FEWS coupled to chemometric methods is a promising technique for the identification of foodborne pathogens as it could provide a rapid identification and classification of the bacteria. [Copyright &y& Elsevier]

Abstract: Interpolated rain-gauge data were compared to Meteosat-based precipitation estimates for sub-Saharan Africa. Validation was carried out using a dataset from a very dense gauge network in South Africa, on a point-to-pixel (PO–PI) as well as on a pixel-to-pixel (PI–PI) basis. Error criteria computed at the gauged pixels indicate that overall the interpolated estimates perform similarly to the satellite-based data: they provide good estimates of lower but underestimate larger precipitation amounts. It is concluded that the satellite estimates are more fitted for the operational modelling of processes such as surface runoff and soil erosion, especially in the developing areas where resources are scarce. [Copyright &y& Elsevier]

• Formation and properties of Te-As-Se glasses were reviewed. • Applications in optics and medical of Te-As-Se fibers were summarized. • Prospects of the applications in medical diagnosis and supercontinuum generation. Te-As-Se (TAS) chalcogenide (ChG) glasses are suitable candidates for mid-infrared (MIR) applications. In this review, the formation, preparation of high-purity TAS glasses and optical fibers, and the applications of TAS fibers were systematically reviewed first. Then, the thermal, optical properties as well as the fiber performance parameters of TAS glasses were summarized. Finally, the progress of TAS fibers in MIR applications, especially for sensor applications such as food monitoring and medical diagnostic were described in detail. [ABSTRACT FROM AUTHOR]

With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data . The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the "supply chain" of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions., (Copyright © 2020 Pitts, Gopal, Ma, Koch, Boumans and Kaufman.)